03-05-2012, 12:39 PM
3D Model Searching
3DSearching .doc (Size: 366.5 KB / Downloads: 34)
Introduction
Over the last decade, tools for acquiring and visualizing 3D models have become integral components of data processing in a number of disciplines, including medicine, chemistry, architecture and entertainment.
With the proliferation of these tools, an explosion in the number of available 3D models has occurred.
3D model searching , a tool which uses several techniques has been introduced in order to retrieve models from the database.
Challenges
The main challenge in supporting 3D shape-based similarity queries is to find a computational representation of shape (a shape descriptor) for which an index can be built and geometric matching can be performed efficiently.
The following properties are desirable for the shape descriptor.
- quick to compute
- concise to store
- easy to index
- invariant under similarity transformations
- insensitive to noise and small extra features
- independent of 3D object representation, tessellation, or genus
- robust to arbitrary topological degeneracy
- discriminating of shape differences at many scales
Retrieval Algorithm
Retrieval algorithms retrieves data from the database by using matching method. They are motivated by two principal concerns.
- The algorithm needs to be discriminating.
- The algorithm need to be efficient in both space and time
In practice, addressing the run-time efficiency requirement is done with the assistance of a shape descriptor which is an abstraction of the 3D model, capturing salient shape information in a structure that is well-suited for comparison.
Conclusion
The performance of text matching method and 2D shape matching method is comparatively less .
3D shape matching method outperforms the text matching method. But an average user will not be able to draw 3D sketches very effectively.
The quality of search results can be increased by combining text with the 3D and 2D query interfaces.
3DSearching .doc (Size: 366.5 KB / Downloads: 34)
Introduction
Over the last decade, tools for acquiring and visualizing 3D models have become integral components of data processing in a number of disciplines, including medicine, chemistry, architecture and entertainment.
With the proliferation of these tools, an explosion in the number of available 3D models has occurred.
3D model searching , a tool which uses several techniques has been introduced in order to retrieve models from the database.
Challenges
The main challenge in supporting 3D shape-based similarity queries is to find a computational representation of shape (a shape descriptor) for which an index can be built and geometric matching can be performed efficiently.
The following properties are desirable for the shape descriptor.
- quick to compute
- concise to store
- easy to index
- invariant under similarity transformations
- insensitive to noise and small extra features
- independent of 3D object representation, tessellation, or genus
- robust to arbitrary topological degeneracy
- discriminating of shape differences at many scales
Retrieval Algorithm
Retrieval algorithms retrieves data from the database by using matching method. They are motivated by two principal concerns.
- The algorithm needs to be discriminating.
- The algorithm need to be efficient in both space and time
In practice, addressing the run-time efficiency requirement is done with the assistance of a shape descriptor which is an abstraction of the 3D model, capturing salient shape information in a structure that is well-suited for comparison.
Conclusion
The performance of text matching method and 2D shape matching method is comparatively less .
3D shape matching method outperforms the text matching method. But an average user will not be able to draw 3D sketches very effectively.
The quality of search results can be increased by combining text with the 3D and 2D query interfaces.